{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "os.chdir('/lustre/scratch/kiviaho/prostate_spatial/')\n",
    "\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata as ad\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from scripts.utils import get_sample_ids_reorder, get_sample_crop_coords, get_sample_id_mask, get_treatment_info, save_to_pickle, load_from_pickle\n",
    "from datetime import datetime   \n",
    "\n",
    "\n",
    "from scipy.stats import fisher_exact\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='white')\n",
    "\n",
    "#from statsmodels.stats.multitest import fdrcorrection\n",
    "from tqdm import tqdm\n",
    "\n",
    "from scipy.stats import zscore, spearmanr\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "samples = get_sample_ids_reorder()\n",
    "sample_crop_coord = get_sample_crop_coords()\n",
    "sample_id_masks = get_sample_id_mask()\n",
    "\n",
    "\n",
    "color_dict = {\n",
    " 'Tumor': '#fc8d62',\n",
    " 'Luminal epithelium': '#8da0cb',\n",
    " 'Basal epithelium': '#66c2a5',\n",
    " 'Club epithelium': '#ffd92f',\n",
    " 'Immune': '#a6d854',\n",
    " 'Endothelium': '#e78ac3',\n",
    " 'Fibroblast': '#e5c494',\n",
    " 'Muscle': '#b3b3b3'\n",
    " }\n",
    "\n",
    "regions = list(color_dict.keys())\n",
    "region_colors = list(color_dict.values())\n",
    "\n",
    "# Load a pandas dataframe with metadata (concatenated adata.obs dataframes)\n",
    "all_obs = pd.read_csv('./data/pathology_filtered_obs_with_regions.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Region-specific markers (differential gene expression analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate differential expression in individual regions and individual samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate DEGs between regions within individual samples\n",
    "# This takes several minutes (method=wilcoxon)\n",
    "for sample in tqdm(samples, desc=\"Processing sample\", unit=\"sample\"):\n",
    "\n",
    "    slide = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')\n",
    "    slide.uns['log1p'][\"base\"] = None # https://github.com/scverse/scanpy/issues/2239\n",
    "\n",
    "    # Remove a group if it has less than 10 spots in it\n",
    "    val_counts = slide.obs['predicted_region'].value_counts()\n",
    "    category_groups = val_counts[~(val_counts < 10)].index.tolist()\n",
    "\n",
    "    sc.tl.rank_genes_groups(slide,groupby='predicted_region', method='wilcoxon',groups=category_groups,random_state=25813063)\n",
    "\n",
    "    slide.write_h5ad('./data/visium_with_regions_and_degs/'+sample+'_with_regions_and_degs.h5ad')\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "masked_sample_ids = get_sample_id_mask()\n",
    "# Take a region one at a time, extract DEG results, save into a list\n",
    "# Create a dictionary and save appropriate dataframes\n",
    "region_degs_dict = {}\n",
    "for region in regions:\n",
    "    deg_res_all = pd.DataFrame()\n",
    "    region_degs_dict[region] = deg_res_all\n",
    "\n",
    "\n",
    "for sample in tqdm(samples, desc=\"Processing sample\", unit=\"sample\"):\n",
    "\n",
    "    slide = sc.read_h5ad(('./data/visium_with_regions_and_degs/'+sample+'_with_regions_and_degs.h5ad'))\n",
    "    \n",
    "    for region in regions:\n",
    "\n",
    "        try:\n",
    "            deg_res = sc.get.rank_genes_groups_df(slide,group=region)\n",
    "\n",
    "            deg_res = deg_res[~deg_res['names'].str.contains('RPL|RPS')] # Remove ribosomal genes\n",
    "            deg_res = deg_res[~deg_res['names'].str.startswith('MT')] # Remove mitochondrial genes\n",
    "            deg_res['sample'] = masked_sample_ids[sample]\n",
    "            deg_res = deg_res[(deg_res['pvals_adj']<0.05) & (deg_res['logfoldchanges']>=1)].reset_index(drop=True)\n",
    "\n",
    "            region_degs_dict[region] = pd.concat([region_degs_dict[region].copy(),deg_res],axis=0)\n",
    "\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "    del slide\n",
    "\n",
    "\n",
    "# Save the sample/region-specific DEGs\n",
    "date = datetime.now().strftime('%y%m%d')\n",
    "save_to_pickle(region_degs_dict,'./data/region_gene_markers_signif_degs_'+date+'.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the results into an excel-file\n",
    "\n",
    "# Create a Pandas Excel writer using the file name\n",
    "writer = pd.ExcelWriter('./supplementary_tables/region_specific_markers_individual_samples.xlsx', engine='xlsxwriter')\n",
    "\n",
    "# Iterate through each key-value pair in the dictionary\n",
    "for key, value in region_degs_dict.items():\n",
    "    # Write each dataframe to a separate sheet in the Excel file\n",
    "    value['sample'] = value['sample'].map(sample_id_masks)\n",
    "    value.to_excel(writer, sheet_name=key)\n",
    "\n",
    "# Save and close the Excel writer\n",
    "writer.save()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fisher's exact test of individual gene markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '240229'\n",
    "region_degs_dict = load_from_pickle('./data/region_gene_markers_signif_degs_'+date+'.pkl')\n",
    "\n",
    "region_sample_counts = {}\n",
    "df_gene_counts = pd.DataFrame()\n",
    "for r in regions:\n",
    "    df_gene_counts = pd.merge(region_degs_dict[r]['names'].value_counts().rename(r),df_gene_counts,left_index=True,right_index=True,how='outer')\n",
    "    region_sample_counts[r] = len(region_degs_dict[r]['sample'].unique())\n",
    "df_gene_counts = df_gene_counts.fillna(0).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [04:52<00:00, 36.57s/region]\n"
     ]
    }
   ],
   "source": [
    "# Take DEG results and perform enrichment analysis:\n",
    "# Check in how many samples this gene is overexpressed in a specific region\n",
    "# Compare this to how many samples the same gene is overexpressed in \n",
    "# If there is a significant enrichment for this marker in a region, it can be considired region specific\n",
    "\n",
    "signif_pval_interactions = {}\n",
    "\n",
    "for reg in tqdm(regions,unit='region'):\n",
    "    \n",
    "    df = df_gene_counts.copy()\n",
    "    other_keys = [c for c in df.columns if c != reg]\n",
    "\n",
    "    n_sample = region_sample_counts[reg]\n",
    "    n_control = pd.Series([region_sample_counts[k] for k in other_keys]).sum()\n",
    "\n",
    "    df = df.sort_index()\n",
    "\n",
    "    # Perform Fisher's exact test on all \n",
    "    for iact in df.index:\n",
    "        a = df[reg].loc[[iact]]\n",
    "        c = df[other_keys].sum(axis=1).loc[[iact]]\n",
    "        b = n_sample-a\n",
    "        d = n_control-c\n",
    "        arr = np.array([a,b,c,d]).reshape(2,2).astype(int)\n",
    "        stat,pval = fisher_exact(arr,alternative='greater')\n",
    "        df.loc[[iact],'pval'] = pval\n",
    "\n",
    "    df['adj_pval'] = multipletests(df['pval'],method='fdr_bh')[1]\n",
    "\n",
    "\n",
    "    df_signif = pd.DataFrame({'interaction':df.index.tolist(),\n",
    "                                'active_in':df[reg].astype(int).tolist(),\n",
    "                                'adj_pval':df['adj_pval'].tolist(),\n",
    "                                'interface':np.repeat(reg,len(df))})\n",
    "\n",
    "    signif_pval_interactions[reg] = df_signif.copy()\n",
    "\n",
    "\n",
    "date = datetime.now().strftime('%y%m%d')\n",
    "save_to_pickle(signif_pval_interactions,'./data/region_gene_markers_fishers_exact_test_'+date+'.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format into dataframes and save into an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the number of regions in each sample and save into a dataframe\n",
    "\n",
    "df = all_obs[['sample_id','predicted_region']].copy()\n",
    "df['sample_id'] = df['sample_id'].map(sample_id_masks)\n",
    "df['sample_id']= df['sample_id'].astype('category').cat.reorder_categories(sample_id_masks.values())\n",
    "df['count'] = 1\n",
    "region_counts_df  = df.groupby(['sample_id','predicted_region']).agg({'count':'sum'})\n",
    "\n",
    "# Save sample-specific region abundances into an excel file\n",
    "writer = pd.ExcelWriter('./supplementary_tables/region_prevalences_in_all_samples.xlsx', engine='xlsxwriter')\n",
    "region_counts_df.to_excel(writer)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format the region Fisher's exact to a dataframe\n",
    "for reg in regions:\n",
    "\n",
    "    n_valid_samples = (region_counts_df.loc[region_counts_df.index.get_level_values(1) == reg]['count'] >=10).sum()\n",
    "    df = signif_pval_interactions[reg].copy()\n",
    "\n",
    "    df = df.drop(columns='interface')\n",
    "    df.columns = ['gene', 'over-expressed in (out of {:d} region-containing samples)'.format(n_valid_samples), 'adjusted p-value (Fishers exact test)']\n",
    "    df = df.sort_values('adjusted p-value (Fishers exact test)').reset_index(drop=True)\n",
    "    signif_pval_interactions[reg] = df\n",
    "\n",
    "\n",
    "# Save the results into an excel-file\n",
    "# Create a Pandas Excel writer using the file name\n",
    "writer = pd.ExcelWriter('./supplementary_tables/supp_table_with_region_marker_fishers.xlsx', engine='xlsxwriter')\n",
    "\n",
    "# Iterate through each key-value pair in the dictionary\n",
    "for key, value in signif_pval_interactions.items():\n",
    "    # Write each dataframe to a separate sheet in the Excel file\n",
    "    value.to_excel(writer, sheet_name=key)\n",
    "\n",
    "# Save and close the Excel writer\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dotplot of canonical region-specific markers (Figure 1c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_degs_dict = load_from_pickle('./data/region_gene_markers_signif_degs_240229.pkl')\n",
    "\n",
    "# Define gene markers \n",
    "marker_genes_for_hmap = ['PRAC1','HPN','PCAT14','AMACR','PCA3', # Tumor\n",
    "                         'MSMB','ACPP','RDH11','NKX3-1','AZGP1', # Luminal\n",
    "                         'KRT5','KRT15','TRIM29','TP63','SLC14A1', # Basal \n",
    "                         'MMP7','PIGR','LTF','CP','KRT7', # Club\n",
    "                         'CXCR4','TRBC1','CD3D','LYZ','CD79A', # Immune\n",
    "                         'EPAS1','EMP1','VWF','PECAM1','IFI27', # Endothelium\n",
    "                         'DCN','LUM','FBLN1','SFRP2','COL1A1', # Fibroblast\n",
    "                         'TAGLN','ACTA2','ACTG2','MYH11','MYL9' # Muscle\n",
    "                         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the dotplot structure\n",
    "plot_df = pd.DataFrame()\n",
    "for region in regions:\n",
    "    degs = region_degs_dict[region]\n",
    "    n_samples = len(degs['sample'].unique())\n",
    "    \n",
    "    degs = degs[degs['names'].isin(marker_genes_for_hmap)]\n",
    "    \n",
    "    mean_lfc_list = []\n",
    "    proportion_degs_list = []\n",
    "\n",
    "    for gene in marker_genes_for_hmap:\n",
    "\n",
    "        if gene in degs['names'].values:\n",
    "            mean_lfc = degs[degs['names'] == gene]['logfoldchanges'].mean()\n",
    "            proportion_degs = degs['names'].value_counts()[gene] / n_samples\n",
    "            mean_lfc_list.append(mean_lfc)\n",
    "            proportion_degs_list.append(proportion_degs)\n",
    "        \n",
    "        else:\n",
    "            mean_lfc_list.append(np.nan)\n",
    "            proportion_degs_list.append(0)\n",
    "    \n",
    "    df = pd.DataFrame({'mean_logfoldchange': mean_lfc_list,'pct_deg_in': proportion_degs_list, 'region': region},index = marker_genes_for_hmap)\n",
    "    plot_df = pd.concat([plot_df,df],axis=0)\n",
    "\n",
    "plot_df['genes'] = plot_df.index.astype(str)\n",
    "\n",
    "plot_df.loc[plot_df['pct_deg_in']==0,'pct_deg_in'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the dotplot\n",
    "sns.set(style='white')\n",
    "\n",
    "width = 18\n",
    "height = 4\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(width, height))\n",
    "yticks_list = list(np.arange(2,(len(regions)*2)+2,2)[::-1])\n",
    "\n",
    "# Get control over interactions order and gap\n",
    "plot_df['region_y'] = plot_df['region'].map(dict(zip(regions,yticks_list)))\n",
    "sns.scatterplot(x='genes', y='region_y', size='pct_deg_in', hue='mean_logfoldchange', \n",
    "                data=plot_df, sizes=(40, 400), palette='Reds', ax=ax,legend=True,\n",
    "                )\n",
    "\n",
    "plt.ylim(0,yticks_list[0]+2)\n",
    "plt.yticks(yticks_list,regions)\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(loc='center left',handlelength=1.5, handleheight=1.5, bbox_to_anchor=(1.05, 0.5))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('./plots/region_gene_markers_dotplot.pdf',transparent=True)\n",
    "plt.show()\n",
    "\n",
    "# Save the plot source data\n",
    "plot_df.to_excel('./source_data/figure_1c.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot to compare region pcts across categories (Figure 2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "treat_info = get_treatment_info()\n",
    "concat_obs = all_obs.copy()\n",
    "concat_obs['treatment'] = concat_obs['sample_id'].map(treat_info)\n",
    "\n",
    "def get_spot_proportions(region,sample_list=samples,obs_df=all_obs,treatment_info=treat_info, re_annotate_treated=True):\n",
    "    df = pd.DataFrame(index=sample_list,columns=[region + ' spots','total spots','treatment'])\n",
    "    for sample in sample_list:\n",
    "        sample_df = obs_df[obs_df['sample_id'] == sample].copy()\n",
    "        df.loc[sample,'total spots'] = len(sample_df)\n",
    "        df.loc[sample,region + ' spots'] = len(sample_df[sample_df['predicted_region'] == region])\n",
    "    df['proportion'] = df[region+' spots'] / df['total spots']\n",
    "    df['treatment'] = df.index.map(treatment_info)\n",
    "\n",
    "    if re_annotate_treated:\n",
    "        # Re-annotate\n",
    "        df['treatment'] = df['treatment'].replace({'bicalutamide':'treated','goserelin':'treated','degarelix':'treated','degarelix_apalutamide':'treated'})\n",
    "\n",
    "    df['treatment'] = df['treatment'].astype('category')    \n",
    "    df['proportion'] = df['proportion'].astype(float)\n",
    "\n",
    "    return(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x216 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='whitegrid')\n",
    "\n",
    "order = ['bph', 'untreated', 'treated','crpc']\n",
    "\n",
    "# All data saved into this\n",
    "plot_df_list = []\n",
    "fig, axs = plt.subplots(1,8,figsize=(12,3))\n",
    "for i, region in enumerate(regions):\n",
    "    ax = axs[i]\n",
    "    df = get_spot_proportions(region)\n",
    "\n",
    "    sns.stripplot(x=\"treatment\", y=\"proportion\", order = order, data=df, size=5, ax=ax, color=color_dict[region]) # color=\".7\",)\n",
    "    sns.boxplot(showmeans=False,\n",
    "            meanline=False,\n",
    "            meanprops={'color': 'k', 'ls': '-', 'lw': 2},\n",
    "            medianprops={'visible': True},\n",
    "            whiskerprops={'visible': True},\n",
    "            zorder=10,\n",
    "            x=\"treatment\",\n",
    "            y=\"proportion\",\n",
    "            data=df,\n",
    "            order = order,\n",
    "            showfliers=False,\n",
    "            showbox=True,\n",
    "            showcaps=True,\n",
    "            color='whitesmoke',\n",
    "            width=0.6,\n",
    "            ax=ax)\n",
    "    ax.set_ylim(-0.1,1.1)\n",
    "    #ax.set_title(region)\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('')\n",
    "    #ax.set_xticklabels([''])\n",
    "    ax.set_xticklabels(ax.get_xticklabels(),rotation=15)\n",
    "    ax.set_yticklabels([''])\n",
    "    \n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "\n",
    "    df = df.rename(columns={f'{region} spots':'spots in region'})\n",
    "    df['region'] = region\n",
    "\n",
    "    plot_df_list.append(df)\n",
    "\n",
    "plot_df = pd.concat(plot_df_list)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./plots/region_proportion_swarmplots/all_region_proportions_swarmplot_boxplot.pdf') # 6.3.2024 added boxplot\n",
    "plt.show()\n",
    "\n",
    "# Save the plot source data\n",
    "plot_df.index = plot_df.index.map(sample_id_masks)\n",
    "plot_df.to_excel('./source_data/figure_2d.xlsx')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tumor\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.028\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    7.2e-03  4.5e-03 1.1e-01\n",
      "untreated 7.2e-03    1.0e+00  9.3e-01 3.7e-01\n",
      "treated   4.5e-03    9.3e-01  1.0e+00 2.4e-01\n",
      "crpc      1.1e-01    3.7e-01  2.4e-01 1.0e+00\n",
      "\n",
      "Luminal epithelium\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.002\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    3.2e-02  4.3e-01 1.0e+00\n",
      "untreated 3.2e-02    1.0e+00  9.2e-04 1.7e-02\n",
      "treated   4.3e-01    9.2e-04  1.0e+00 3.5e-01\n",
      "crpc      1.0e+00    1.7e-02  3.5e-01 1.0e+00\n",
      "\n",
      "Basal epithelium\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.004\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    7.3e-02  3.2e-01 1.0e+00\n",
      "untreated 7.3e-02    1.0e+00  4.4e-04 1.7e-01\n",
      "treated   3.2e-01    4.4e-04  1.0e+00 2.6e-01\n",
      "crpc      1.0e+00    1.7e-01  2.6e-01 1.0e+00\n",
      "\n",
      "Club epithelium\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.470\n",
      "\n",
      "Immune\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.043\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    4.9e-02  5.2e-01 1.4e-01\n",
      "untreated 4.9e-02    1.0e+00  2.0e-02 3.7e-01\n",
      "treated   5.2e-01    2.0e-02  1.0e+00 1.5e-01\n",
      "crpc      1.4e-01    3.7e-01  1.5e-01 1.0e+00\n",
      "\n",
      "Endothelium\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.015\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    1.6e-02  2.8e-02 8.6e-02\n",
      "untreated 1.6e-02    1.0e+00  4.1e-02 4.6e-01\n",
      "treated   2.8e-02    4.1e-02  1.0e+00 1.3e-01\n",
      "crpc      8.6e-02    4.6e-01  1.3e-01 1.0e+00\n",
      "\n",
      "Fibroblast\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.013\n",
      "Wilcoxon rank-sum test:\n",
      "              bph  untreated  treated    crpc\n",
      "bph       1.0e+00    5.5e-03  5.5e-02 1.4e-01\n",
      "untreated 5.5e-03    1.0e+00  2.0e-02 3.7e-01\n",
      "treated   5.5e-02    2.0e-02  1.0e+00 6.6e-01\n",
      "crpc      1.4e-01    3.7e-01  6.6e-01 1.0e+00\n",
      "\n",
      "Muscle\n",
      "Kruskal-Wallis test\n",
      "p-value: 0.062\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform statistical testing of possible differences in spot proportions between samples\n",
    "# Use these to draw significance bars on top of the swarmplots\n",
    "\n",
    "from scipy.stats import kruskal,ranksums\n",
    "\n",
    "# Set to print the dataframe p-values as a specific format\n",
    "pd.set_option('display.float_format', lambda x: '%.1e' % x)\n",
    "\n",
    "for region in regions:\n",
    "    df = get_spot_proportions(region)\n",
    "    df['treatment'] = df['treatment'].cat.reorder_categories(['bph','untreated','treated','crpc'])\n",
    "\n",
    "    # extract the values and categories from the dataframe\n",
    "    values = df['proportion']\n",
    "    categories = df['treatment']\n",
    "\n",
    "    # perform the Kruskal-Wallis test\n",
    "    statistic, p_value = kruskal(*[values[categories == cat] for cat in set(categories)])\n",
    "\n",
    "    print(region)\n",
    "    # print the results\n",
    "    print(\"Kruskal-Wallis test\")\n",
    "    print(\"p-value: {:.3f}\".format(p_value))\n",
    "\n",
    "    if p_value < 0.05:\n",
    "        treatments = df['treatment'].cat.categories\n",
    "        res_df = pd.DataFrame(np.eye(len(treatments),len(treatments)),index=treatments,columns=treatments)\n",
    "\n",
    "        for i in range(len(treatments)):\n",
    "            for j in range(i+1, len(treatments)):\n",
    "                # Get data for the two treatments being compared\n",
    "                data1 = df.loc[df['treatment'] == treatments[i], 'proportion']\n",
    "                data2 = df.loc[df['treatment'] == treatments[j], 'proportion']\n",
    "                \n",
    "                # Perform Mann-Whitney U test\n",
    "                stat, pval = ranksums(data1, data2)\n",
    "                \n",
    "                # Print results\n",
    "                res_df.loc[treatments[i],treatments[j]] = pval\n",
    "                res_df.loc[treatments[j],treatments[i]] = pval\n",
    "        print('Wilcoxon rank-sum test:')\n",
    "        print(res_df)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential expression TRNA vs NEADT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Done in scripts/neadt_vs_trna_degs_script.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the results and check genes\n",
    "\n",
    "region_degs_dict = {}\n",
    "\n",
    "for region in regions:\n",
    "    df = pd.read_excel('./supplementary_tables/NEADT_vs_TRNA_degs_by_region.xlsx',sheet_name=region,index_col=0)\n",
    "    region_degs_dict[region] = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save only those that pass the DEG threshold\n",
    "# Create a Pandas Excel writer using the file name\n",
    "writer = pd.ExcelWriter('./supplementary_tables/NEADT_vs_TRNA_degs_by_region_log2fc_filtered.xlsx', engine='xlsxwriter')\n",
    "\n",
    "# Iterate through each key-value pair in the dictionary\n",
    "for key, value in region_degs_dict.items():\n",
    "    # Write each dataframe to a separate sheet in the Excel file\n",
    "    value = value[(value['pvals_adj'] < 0.05) & (np.abs(value['logfoldchanges']) >= 1)].reset_index(drop=True)\n",
    "\n",
    "    value.to_excel(writer, sheet_name=key)\n",
    "\n",
    "# Save and close the Excel writer\n",
    "writer.save()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial plots in a grid (Supplementary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UMI counts on spatial sections 6x8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Row completion: 100%|██████████| 6/6 [00:52<00:00,  8.75s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='white')\n",
    "\n",
    "it=0\n",
    "fig, axs = plt.subplots(6, 8, figsize=(16, 12),dpi=300)\n",
    "\n",
    "for i in tqdm(range(6),desc='Row completion'):\n",
    "    for j in range(8):\n",
    "        \n",
    "        if it < len(samples):\n",
    "\n",
    "            sample_name = samples[it]\n",
    "            \n",
    "            slide = sc.read_h5ad('./data/visium_with_regions/'+sample_name+'_with_regions.h5ad')\n",
    "            sc.pp.calculate_qc_metrics(slide,layer='counts',inplace=True)\n",
    "\n",
    "            if 'P320' in sample_name:\n",
    "                sc.pl.spatial(slide,color='total_counts',\n",
    "                        #crop_coord=sample_crop_coord[sample_name],\n",
    "                        title='',\n",
    "                        size=1.5, alpha_img=0, legend_loc=None,\n",
    "                        ax=axs[i,j],\n",
    "                        show=False,\n",
    "                        cmap='viridis',\n",
    "                        colorbar_loc=None,\n",
    "                        vmax=25000\n",
    "                        )\n",
    "            else:\n",
    "                sc.pl.spatial(slide,color='total_counts',\n",
    "                        title='',\n",
    "                        crop_coord=sample_crop_coord[sample_name],\n",
    "                        size=1.5, alpha_img=0, legend_loc=None,\n",
    "                        ax=axs[i,j],\n",
    "                        show=False,\n",
    "                        cmap='viridis',\n",
    "                        colorbar_loc=None,\n",
    "                        vmax=25000\n",
    "                        )\n",
    "                \n",
    "            del slide\n",
    "                \n",
    "            axs[i,j].set_xlabel(None)\n",
    "            axs[i,j].set_ylabel(None)\n",
    "            it+=1\n",
    "        else:\n",
    "            axs[i,j].set_visible(False)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.savefig('./plots/UMI_counts_all_samples_spatial.pdf',dpi=120)\n",
    "plt.clf()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regions on spatial sections 6x8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Row completion: 100%|██████████| 6/6 [00:39<00:00,  6.63s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='white')\n",
    "\n",
    "it=0\n",
    "fig, axs = plt.subplots(6, 8, figsize=(16, 12))\n",
    "\n",
    "for i in tqdm(range(6),desc='Row completion'):\n",
    "    for j in range(8):\n",
    "        \n",
    "        if it < len(samples):\n",
    "\n",
    "            sample_name = samples[it]\n",
    "            \n",
    "            slide = sc.read_h5ad('./data/visium_with_regions/'+sample_name+'_with_regions.h5ad')\n",
    "\n",
    "            if 'P320' in sample_name:\n",
    "\n",
    "                sc.pl.spatial(slide,color='predicted_region',title='',\n",
    "                    #    crop_coord=sample_crop_coord[sample_name],\n",
    "                        size=1.5, alpha_img=0.0, legend_loc=None, # Old 1.3 and 0.8\n",
    "                        ax=axs[i,j],show=False\n",
    "                        )\n",
    "            else:\n",
    "                sc.pl.spatial(slide,color='predicted_region',title='',\n",
    "                        crop_coord=sample_crop_coord[sample_name],\n",
    "                        size=1.5, alpha_img=0.0, legend_loc=None, # Old 1.3 and 0.8\n",
    "                        ax=axs[i,j],show=False\n",
    "                        )\n",
    "                \n",
    "            del slide\n",
    "                \n",
    "            axs[i,j].set_xlabel(None)\n",
    "            axs[i,j].set_ylabel(None)\n",
    "            it+=1\n",
    "        else:\n",
    "            axs[i,j].set_visible(False)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.savefig('./plots/regions_all_samples_spatial_6x8.pdf',dpi=120)\n",
    "plt.clf()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tissue H&E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style='white')\n",
    "sample_id_mask_dict = get_sample_id_mask()\n",
    "samples = list(sample_id_mask_dict.keys())\n",
    "\n",
    "it=0\n",
    "fig, axs = plt.subplots(10, 5, figsize=(15, 30),dpi=300)\n",
    "\n",
    "for i in tqdm(range(10),desc='Row completion'):\n",
    "    for j in range(5):\n",
    "        \n",
    "        if it < len(samples):\n",
    "\n",
    "            sample_name = samples[it]\n",
    "            \n",
    "            slide = sc.read_h5ad('./data/visium_with_regions/'+sample_name+'_with_regions.h5ad')\n",
    "\n",
    "            if 'P320' in sample_name:\n",
    "                sc.pl.spatial(slide,color='predicted_region',title=sample_id_mask_dict[sample_name],\n",
    "                        #    crop_coord=sample_crop_coord[samples[it]],\n",
    "                            size=0, alpha_img=1, legend_loc=None,\n",
    "                            ax=axs[i,j],show=False\n",
    "                            )\n",
    "\n",
    "            else:\n",
    "                sc.pl.spatial(slide,color='predicted_region',title=sample_id_mask_dict[sample_name],\n",
    "                        crop_coord=sample_crop_coord[sample_name],\n",
    "                        size=0, alpha_img=1, legend_loc=None,\n",
    "                        ax=axs[i,j],show=False\n",
    "                        )\n",
    "                \n",
    "            del slide\n",
    "                \n",
    "            axs[i,j].set_xlabel(None)\n",
    "            axs[i,j].set_ylabel(None)\n",
    "            it+=1\n",
    "        else:\n",
    "            axs[i,j].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.savefig('./plots/HE_all_samples_spatial.png',dpi=120)\n",
    "plt.clf()\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regions one sample at a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample in tqdm(samples, desc=\"Processing sample\", unit=\"sample\"):\n",
    "    \n",
    "    it=0\n",
    "\n",
    "    fig, axs = plt.subplots(2, 4, figsize=(12, 6),dpi=300)\n",
    "\n",
    "    slide = sc.read_h5ad('./data/visium_with_regions/'+sample+'_with_regions.h5ad')\n",
    "\n",
    "    for i in range(2):\n",
    "        for j in range(4):\n",
    "        \n",
    "            region_of_interest = regions[it]\n",
    "\n",
    "            if region_of_interest in slide.obs['predicted_region'].unique():\n",
    "                \n",
    "                slide.obs['region_of_interest'] = np.nan\n",
    "\n",
    "                slide.obs.loc[slide.obs['predicted_region']==region_of_interest,'region_of_interest'] = region_of_interest\n",
    "\n",
    "                slide.uns['region_of_interest_colors'] = [color_dict[region_of_interest]]\n",
    "\n",
    "                slide.obs['region_of_interest'] = slide.obs['region_of_interest'].astype('category')\n",
    "\n",
    "            \n",
    "                n_spots = slide.obs['region_of_interest'].value_counts()[region_of_interest]\n",
    "\n",
    "                sc.pl.spatial(slide,color='region_of_interest',title=region_of_interest+ ' ({:d})'.format(n_spots),\n",
    "                        crop_coord=sample_crop_coord[sample],\n",
    "                        size=1.5, alpha_img=0.0, legend_loc=None,na_color='whitesmoke', # Changed from 0.8\n",
    "                        ax=axs[i,j],show=False\n",
    "                        )\n",
    "                axs[i,j].set_xlabel(None)\n",
    "                axs[i,j].set_ylabel(None)\n",
    "\n",
    "            else:\n",
    "                axs[i,j].set_visible(False)\n",
    "            \n",
    "            it+=1\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('./plots/all_samples_grid/region_based/sample_specific/'+sample+'_all_regions_individually.pdf')\n",
    "    plt.clf()\n",
    "    #plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "squidpy",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
